1. Field of the Invention
This invention relates to an improved method and apparatus for controlling a process parameter and more particularly to a method and apparatus in which one of several stored control algorithms is selected for use in controlling the parameter.
2. Description of the Prior Art
In controlling industrial processes, significant losses may result from inability to maintain process parameters such as flow, temperature, and concentration, at their most efficient or optimum value. In monetary terms, these losses have been estimated as from more than 5% to over 12% of the total product value. In addition, excessive fluctuation of variables may lead to early wearout or failure of equipment, requiring costly shut-downs.
A large portion of these losses are due to a less-than-ideal match of a process controller with the process dynamics and with the nature of a disturbance or change in process parameter. For a closed-loop response of a process to be optimum in some sense (say minimum time and distance from the target value), the controller dynamics should be designed to suit both the plant and the disturbance.
The most common prior art approach to this problem is the employment of an "average" or "compromise" controller algorithm such as the so-called three-term or proportional-integral-differential (PID) algorithm. This algorithm can be "tuned" to suit a particular process and a particular signal-noise environment by adjusting the gain parameters of the proportional, integral and differential elements combining to produce the output signal for controlling the process. Tuning is accomplished by somewhat empirical methods.
Since the tuning of a conventional controller is a compromise solution, it seldom, if ever, yields ideal performance and leads to much of the losses cited above.
Another approach is to use the so-called "self-tuning" or continuously adaptive controller. This senses changes in the closed-loop dynamics and adjusts some non-linear element in the controller to return the response to a more nearly ideal state.
This type of controller algorithm works well when there are relatively slow changes in dynamics caused by environmental changes or drift. However, it is unsuited to respond to rapid changes in disturbance. Also, adaptive algorithms tend to be quite complex and/or to require the injection of extraneous disturbances at intervals, neither of which is desired in industrial process control.